Document Type



Master of Science (MS)


Electrical Engineering

First Advisor's Name

Ismail Guvenc

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Leonardo Bobadilla

Second Advisor's Committee Title

committee member

Third Advisor's Name

Selcuk Uluagac

Third Advisor's Committee Title

committee member


UAV, search and rescue, machine learning, localization, flight pattern, search theory method, probe requests, maximizing UAV flight time, autonomous, radio-control

Date of Defense



This thesis presents how unmanned aerial vehicles (UAVs) can successfully assist in search and rescue (SAR) operations using wireless localization. The zone-grid to partition to capture/detect WiFi probe requests follows the concepts found in Search Theory Method. The UAV has attached a sensor, e.g., WiFi sniffer, to capture/detect the WiFi probes from victims or lost people’s smartphones. Applying the Random-Forest based machine learning algorithm, an estimation of the user's location is determined with a 81.8% accuracy.

UAV technology has shown limitations in the navigational performance and limited flight time. Procedures to optimize these limitations are presented. Additionally, how the UAV is maneuvered during flight is analyzed, considering different SAR flight patterns and Li-Po battery consumption rates of the UAV. Results show that controlling the UAV by remote-controll detected the most probes, but it is less power efficient compared to control it autonomously.





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